Systems and methods for estimating reservoir productivity as a function of position in a subsurface volume of interest

ABSTRACT

Systems and methods for estimating reservoir productivity as a function of position in a subsurface volume of interest are disclosed. Exemplary implementations may: obtain subsurface data and well data corresponding to a subsurface volume of interest; obtain a parameter model; use the subsurface data and the well data to generate multiple production parameter maps; apply the parameter model to the multiple production parameter maps to generate refined production parameter values; generate multiple refined production parameter graphs; display the multiple refined production parameter graphs; generate one or more user input options; receive a defined well design and the one or more user input options selected by a user to generate limited production parameter values; generate a representation of estimated reservoir productivity as a function of position in the subsurface volume of interest using the defined well design and visual effects; and display the representation.

FIELD OF THE DISCLOSURE

The present disclosure relates to systems and methods for estimatingreservoir productivity as a function of position in a subsurface volumeof interest.

SUMMARY

An aspect of the present disclosure relates to a method for estimatingreservoir productivity as a function of position in a subsurface volumeof interest. The method may include obtaining, from the non-transientelectronic storage, subsurface data and well data corresponding to asubsurface volume of interest. The subsurface data and the well data mayinclude production parameter values for multiple production parametersas a function of position in the subsurface volume of interest, therebycharacterizing subsurface production features that affect the reservoirproductivity. The method may include obtaining, from the non-transientelectronic storage, a parameter model. The parameter model may betrained using training data on an initial parameter model. The trainingdata may include well data and the production parameter values forcorresponding multiple production parameters affecting productivity ofthe one or more wells as a function of position in the subsurface volumeof interest. The method may include using, with the one or more physicalcomputer processors, the subsurface data and the well data to generatemultiple production parameter maps. A given production parameter map mayrepresent the production parameter values for a given productionparameter as a function of time and position in the subsurface volume ofinterest. The method may include applying, with the one or more physicalcomputer processors, the parameter model to the multiple productionparameter maps to generate refined production parameter values. Themethod may include generating, with the one or more physical computerprocessors, multiple refined production parameter graphs from therefined production parameter values wherein a given refined productionparameter graph specifies the refined production parameter values for acorresponding production parameter as a function of estimated reservoirproductivity. The method may include displaying, via the graphical userinterface, the multiple refined production parameter graphs. The methodmay include generating, with the one or more physical computerprocessors, one or more user input options to define a well design andlimit the refined production parameter values corresponding toindividual ones of the multiple refined production parameters. Themethod may include receiving, via the graphical user interface, adefined well design and the one or more user input options selected by auser to limit the refined production parameter values corresponding tothe multiple refined production parameter graphs to generate limitedproduction parameter values. The method may include generating, with theone or more physical computer processors, a representation of estimatedreservoir productivity as a function of position in the subsurfacevolume of interest using the defined well design and visual effects todepict at least a portion of the limited production parameter values,based on the one or more user input options selected. The method mayinclude displaying, via the graphical user interface, therepresentation.

An aspect of the present disclosure relates to a system configured forestimating reservoir productivity as a function of position in asubsurface volume of interest. The system may include one or morehardware processors configured by machine-readable instructions. Theprocessor(s) may be configured to obtain, from the non-transientelectronic storage, subsurface data and well data corresponding to asubsurface volume of interest. The subsurface data and the well data mayinclude production parameter values for multiple production parametersas a function of position in the subsurface volume of interest, therebycharacterizing subsurface production features that affect the reservoirproductivity. The processor(s) may be configured to obtain, from thenon-transient electronic storage, a parameter model. The parameter modelmay be trained using training data on an initial parameter model. Thetraining data may include well data and the production parameter valuesfor corresponding multiple production parameters affecting productivityof the one or more wells as a function of position in the subsurfacevolume of interest. The processor(s) may be configured to use, with theone or more physical computer processors, the subsurface data and thewell data to generate multiple production parameter maps. A givenproduction parameter map may represent the production parameter valuesfor a given production parameter as a function of time and position inthe subsurface volume of interest. The processor(s) may be configured toapply, with the one or more physical computer processors, the parametermodel to the multiple production parameter maps to generate refinedproduction parameter values. The processor(s) may be configured togenerate, with the one or more physical computer processors, multiplerefined production parameter graphs from the refined productionparameter values wherein a given refined production parameter graphspecifies the refined production parameter values for a correspondingproduction parameter as a function of estimated reservoir productivity.The processor(s) may be configured to display, via the graphical userinterface, the multiple refined production parameter graphs. Theprocessor(s) may be configured to generate, with the one or morephysical computer processors, one or more user input options to define awell design and limit the refined production parameter valuescorresponding to individual ones of the multiple refined productionparameters. The processor(s) may be configured to receive, via thegraphical user interface, a defined well design and the one or more userinput options selected by a user to limit the refined productionparameter values corresponding to the multiple refined productionparameter graphs to generate limited production parameter values. Theprocessor(s) may be configured to generate, with the one or morephysical computer processors, a representation of estimated reservoirproductivity as a function of position in the subsurface volume ofinterest using the defined well design and visual effects to depict atleast a portion of the limited production parameter values, based on theone or more user input options selected. The processor(s) may beconfigured to display, via the graphical user interface, therepresentation.

These and other features, and characteristics of the present technology,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, will become more apparent upon consideration of thefollowing description and the appended Claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the presentlydisclosed technology. As used in the specification and in the Claims,the singular form of “a”, “an”, and “the” include plural referentsunless the context clearly dictates otherwise.

The technology disclosed herein, in accordance with one or more variousimplementations, is described in detail with reference to the followingfigures. The drawings are provided for purposes of illustration only andmerely depict typical or example implementations of the disclosedtechnology. These drawings are provided to facilitate the reader'sunderstanding of the disclosed technology and shall not be consideredlimiting of the breadth, scope, or applicability thereof. It should benoted that for clarity and ease of illustration these drawings are notnecessarily made to scale.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A shows a system configured for estimating reservoir productivityas a function of position in a subsurface volume of interest, inaccordance with one or more implementations.

FIG. 1B illustrates a flowchart of a method of hydrocarbon reservoirrecoverable pay characterization, in accordance with someimplementations.

FIG. 2A illustrates example training for a parameter model, inaccordance with some implementations.

FIG. 2B illustrates example training for a parameter model, inaccordance with some implementations.

FIG. 2C illustrates example training for a parameter model, inaccordance with some implementations.

FIG. 3 illustrates example Boruta plots identifying an effect productionparameters may have on estimated reservoir productivity, in accordancewith one or more implementations.

FIG. 4 illustrates example Boruta plots identifying an effect productionparameters may have on estimated reservoir productivity by month, inaccordance with one or more implementations.

FIG. 5 illustrates example production parameter graphs, in accordancewith one or more implementations.

FIG. 6A shows example map results of estimated reservoir productivityover a 12 month interval, in accordance with some implementations.

FIG. 6B shows example map results of estimated reservoir productivityover a 12 month interval, in accordance with some implementations.

FIG. 6C shows example map results of estimated reservoir productivityover a 12 month interval, in accordance with some implementations.

FIG. 6D shows example map results of estimated reservoir productivityover a 12 month interval, in accordance with some implementations.

FIG. 6E shows example map results of estimated reservoir productivityover a 12 month interval, in accordance with some implementations.

FIG. 6F shows example map results of estimated reservoir productivityover a 12 month interval, in accordance with some implementations.

FIG. 6G shows example map results of estimated reservoir productivityover a 12 month interval, in accordance with some implementations.

FIG. 6H shows example map results of estimated reservoir productivityover a 12 month interval, in accordance with some implementations.

FIG. 6I shows example map results of estimated reservoir productivityover a 12 month interval, in accordance with some implementations.

FIG. 6J shows example map results of estimated reservoir productivityover a 12 month interval, in accordance with some implementations.

FIG. 6K shows example map results of estimated reservoir productivityover a 12 month interval, in accordance with some implementations.

FIG. 6L shows example map results of estimated reservoir productivityover a 12 month interval, in accordance with some implementations.

FIG. 7A illustrates example type curve generation and decline analysesused to estimate reservoir productivity compared to actual productivity,in accordance with some implementations.

FIG. 7B illustrates example type curve generation and decline analysesused to estimate reservoir productivity compared to actual productivity,in accordance with some implementations.

FIG. 7C illustrates example type curve generation and decline analysesused to estimate reservoir productivity compared to actual productivity,in accordance with some implementations.

FIG. 7D illustrates example type curve generation and decline analysesused to estimate reservoir productivity compared to actual productivity,in accordance with some implementations.

FIG. 7E illustrates example type curve generation and decline analysesused to estimate reservoir productivity compared to actual productivity,in accordance with some implementations.

FIG. 8 is an example output of the disclosed technology, in accordancewith one or more implementations, in accordance with someimplementations.

FIG. 9 includes a flow chart of a method for estimating reservoirproductivity as a function of position in a subsurface volume ofinterest, in accordance with one or more implementations.

FIG. 10 illustrates a workflow for estimating productivity of a welllocation as a function of position in a subsurface volume of interest,in accordance with one or more implementations.

FIG. 11A illustrates example production parameter graphs, in accordancewith one or more implementations.

FIG. 11B illustrates example production parameter graphs, in accordancewith one or more implementations.

FIG. 11C illustrates example production parameter graphs, in accordancewith one or more implementations.

FIG. 11D illustrates example production parameter graphs, in accordancewith one or more implementations.

DETAILED DESCRIPTION

Well planning in hydrocarbon reservoirs may require characterization ofthe reservoir, including an understanding of the rock properties.Previous approaches for pay characterization often focus on hydrocarbonstorage capability or may rely on inferential relationships to wellproductivity. More recent approaches may utilize simple linear andnon-linear multivariate regression techniques to characterize therelationship between rock properties, completion strategies, and wellproduction performance, but these methods may be prone to overfitting,have difficulty capturing complex interaction structures in noisyreservoir data, and generally fall short of characterizing the rockproperties that may correspond to enhanced production performance.

There exists a need for improved characterization of subsurfacereservoirs, allowing production predictions across the field as well astype curve generation.

Disclosed below are methods, systems, and computer readable storagemedia that provide an estimation of reservoir productivity as a functionof position in a subsurface volume of interest.

Reference will now be made in detail to various implementations,examples of which are illustrated in the accompanying drawings. In thefollowing detailed description, numerous details may be set forth inorder to provide a thorough understanding of the present disclosure andthe implementations described herein. However, implementations describedherein may be practiced without such details. In other instances, somemethods, procedures, components, and mechanical apparatuses may not bedescribed in detail, so as not to unnecessarily obscure aspects of theimplementations.

The presently disclosed technology includes implementations of a methodand system for estimated reservoir productivity in a subsurface volumeof interest, allowing better hydrocarbon exploration, prospectidentification, development and economic planning, such as, for example,for unconventional and tight rock plays. A subsurface volume of interestmay include any area, region, and/or volume underneath a surface. Such avolume may include, or be bounded by, one or more of a water surface, aground surface, and/or other surfaces. The method may link keyreservoir, completion, and development strategy (e.g. well spacing)characteristics with long-term well production using a predictive dataanalytic approach. The method may be designed to quantify the expectedrecovery across subsurface volume of interest given historicalcorrespondences between production and reservoir and completionpractices. The method may provide all the requisite output to constructpredicted, synthetic type curves and perform decline analysis todetermine an estimated reservoir productivity for any position in thesubsurface volume of interest.

FIG. 1A illustrates a system 100 configured for estimating reservoirproductivity as a function of position in a subsurface volume ofinterest, in accordance with one or more implementations. In someimplementations, system 100 may include one or more servers 102.Server(s) 102 may be configured to communicate with one or more clientcomputing platforms 104 according to a client/server architecture and/orother architectures. Client computing platform(s) 104 may be configuredto communicate with other client computing platforms via server(s) 102and/or according to a peer-to-peer architecture and/or otherarchitectures. Users may access system 100 via client computingplatform(s) 104.

Server(s) 102 may be configured by machine-readable instructions 106.Machine-readable instructions 106 may include one or more instructioncomponents. The instruction components may include computer programcomponents. The instruction components may include one or more of asubsurface data and well data component 108, a parameter model component110, a production parameter graph component 112, a user input component114, a representation component 116, and/or other instructioncomponents.

Subsurface data and well data component 108 may be configured to obtain,from the non-transient electronic storage, subsurface data and well datacorresponding to a subsurface volume of interest. The subsurface dataand/or the well data may be obtained from the non-transient electronicstorage and/or other sources. The subsurface data and the well data mayinclude production parameter values for multiple production parametersas a function of position in the subsurface volume of interest, therebycharacterizing subsurface production features that affect the reservoirproductivity.

The subsurface data and the well data may be filtered by one or more payzones. The subsurface data may include geological data and reservoirdata. Geological data may include petrophysical, core, cutting,pressure, drilling property, mudlog, seismic properties, and/or othergeological data. In implementations, for unconventional reservoirs, thismay include an anticipated stimulated rock volume, a natural geologictarget zone, or even a gross formation interval. In someimplementations, reservoir data may be interpolated using cokriging,autocorrelation gridding techniques, and/or other techniques. Well datamay include completion data and production data. Completion data mayinclude well perforation lengths, proppant intensity, fluid types, wellspacing, number of frac stages, and/or other completion data. Productiondata may include cumulative oil, gas, and/or water production atdifferent time intervals, such as, for example, 6 month or 18 monthcumulative standard barrels of oil equivalent produced.

By way of non-limiting example, the subsurface production features mayinclude one or more petrophysical, core, cutting, pressure, drillingproperty, mudlog, seismic features, well perforation lengths, proppantintensity, fluid types, well spacing, number of fracturing stages,cumulative oil production over a time interval, cumulative gasproduction over a time interval, cumulative water production over a timeinterval, and/or other features.

Subsurface data and well data component 108 may be configured to use thesubsurface data and the well data to generate multiple productionparameter maps. This may be accomplished by one or more physicalcomputer processors. A given production parameter map may represent theproduction parameter values for a given production parameter as afunction of time and position in the subsurface volume of interest.

In implementations, production parameter values may be filtered based onstatistical significance and/or collinearity using, for example, aPearson correlation matrix.

Parameter model component 110 may be configured to obtain a parametermodel. The parameter model may be obtained from the non-transientelectronic storage and/or other sources. The parameter model may betrained using training data on an initial parameter model. The trainingdata may include well data and the production parameter values forcorresponding multiple production parameters affecting productivity ofthe one or more wells as a function of position in the subsurface volumeof interest. The parameter model may include random forest machinelearning and/or other machine learning.

For example, FIGS. 2A, 2B, and 2C illustrate example training for aparameter model, in accordance with some implementations. Referring toFIG. 2A, the parameter model may include random forest machine learning.Random forest machine learning may have a low risk of overfitting, mayallow extreme randomization, and may be very iterative. Random forestmay be a modification of bootstrap aggregation that builds on a largecollection of de-correlated regression trees and then averages them.Bootstrap aggregation may average many noisy but unbiased models toreduce prediction variance. Regression trees may be appropriate forbootstrap aggregation, because they can capture complex interactionstructure. Referring to FIG. 2B, the random forest machine learning usesmany boot strap sets and many regression trees to generate manypredictions, ultimately averaged together to provide the finalprediction algorithm. This identifies the most impactful andstatistically significant predictor production parameters that accountfor differences in well production. Referring to FIG. 2C, applying theparameter model to the multiple refined production parameter maps mayallow for validation of the analytic model via blind testing.

Referring to FIG. 1A, parameter model component 110 may be configured toapply the parameter model to the multiple production parameter maps togenerate multiple refined production parameters including refinedproduction parameter values. This may be accomplished by the one or morephysical computer processors. The refined production parameters may be asubset of the multiple production parameters. The parameter model mayhave been trained, as described herein, to identify one or more of themultiple production parameters that have the greatest effect onproductivity compared to the other multiple production parameters.

In implementations, a Boruta plot may be generated from the randomforest model using the refined production parameters and correspondingrefined production parameter values.

Production parameter graph component 112 may be configured to generatemultiple refined production parameter graphs from the refined productionparameter values wherein a given refined production parameter graphspecifies the refined production parameter values for a correspondingproduction parameter as a function of estimated reservoir productivity.This may be accomplished by the one or more physical computerprocessors.

Production parameter graph component 112 may be configured to displaythe multiple refined production parameter graphs. The multiple refinedproduction parameter graphs may be displayed on a graphical userinterface and/or other displays.

In implementations, production parameter graph component 112 may beconfigured to determine or identify trends, thresholds, and/or otherconditions to limit the refined production parameter values using linearanalysis, non-linear analysis, rate of change analysis, machinelearning, and/or other techniques.

User input component 114 may be configured to generate one or more userinput options to limit the refined production parameter valuescorresponding to individual ones of the multiple refined productionparameters. This may be accomplished by the one or more physicalcomputer processors. By way of non-limiting example, user input optionsmay include a window input for text, numbers, and/or symbols; options toselect greater than, greater than or equal to, less than, and/or lessthan or equal to; note a trend of increasing values, a trend ofdecreasing values; note a linear trend, a non-linear trend, and/or othertrends, options to select one or more threshold values; and/or othertrends. In implementations, user input options may include defining awell design or completion design. A well design may include designparameters used to extract hydrocarbons from a reservoir. The designparameters may include, for example, proppant intensity, fluidintensity, lateral spacing, and/or other design parameters.

User input component 114 may be configured to present the one or moreuser input options corresponding to the multiple refined productionparameters. The one or more user input options may be displayed on agraphical user interface and/or other displays.

User input component 114 may be configured to receive a defined welldesign and the one or more user input options selected by a user tolimit the refined production parameter values corresponding to themultiple refined production parameter graphs to generate limitedproduction parameter values. This may be accomplished by the one or morephysical computer processors. The defined well design may describe thedesign parameters for extracting hydrocarbons, as described above. Thelimited production parameter values may be a subset of the refinedproduction parameter values. As described herein, the limited productionparameter values may be limited based on the thresholds and/or trends ofthe multiple refined production parameter graphs identified by thesystem or by a user through the user input options.

Representation component 116 may be configured to generate arepresentation of estimated reservoir productivity as a function ofposition in the subsurface volume of interest using the defined welldesign and visual effects to depict at least a portion of the limitedproduction parameter values, based on the one or more user input optionsselected. This may be accomplished by the one or more physical computerprocessors. The representation may estimate a productivity of one ormore pay zones of a reservoir in the subsurface volume of interest. Therepresentation may change as a function of time.

In some implementations, a visual effect may include one or more visualtransformation of the representation. A visual transformation mayinclude one or more visual changes in how the representation ispresented or displayed. In some implementations, a visual transformationmay include one or more of a visual zoom, a visual filter, a visualrotation, and/or a visual overlay (e.g., text and/or graphics overlay).

Representation component 116 may be configured to display therepresentation. The representation may be displayed on a graphical userinterface and/or other displays.

In some implementations, server(s) 102, client computing platform(s)104, and/or external resources 130 may be operatively linked via one ormore electronic communication links. For example, such electroniccommunication links may be established, at least in part, via a networksuch as the Internet and/or other networks. It will be appreciated thatthis is not intended to be limiting, and that the scope of thisdisclosure includes implementations in which server(s) 102, clientcomputing platform(s) 104, and/or external resources 130 may beoperatively linked via some other communication media.

A given client computing platform 104 may include one or more processorsconfigured to execute computer program components. The computer programcomponents may be configured to enable an expert or user associated withthe given client computing platform 104 to interface with system 100and/or external resources 130, and/or provide other functionalityattributed herein to client computing platform(s) 104. By way ofnon-limiting example, the given client computing platform 104 mayinclude one or more of a desktop computer, a laptop computer, a handheldcomputer, a tablet computing platform, a NetBook, a Smartphone, a gamingconsole, and/or other computing platforms.

External resources 130 may include sources of information outside ofsystem 100, external entities participating with system 100, and/orother resources. In some implementations, some or all of thefunctionality attributed herein to external resources 130 may beprovided by resources included in system 100.

Server(s) 102 may include electronic storage 132, one or more processors134, and/or other components. Server(s) 102 may include communicationlines, or ports to enable the exchange of information with a networkand/or other computing platforms. Illustration of server(s) 102 in FIG.1A is not intended to be limiting. Server(s) 102 may include a pluralityof hardware, software, and/or firmware components operating together toprovide the functionality attributed herein to server(s) 102. Forexample, server(s) 102 may be implemented by a cloud of computingplatforms operating together as server(s) 102.

Electronic storage 132 may comprise non-transitory storage media thatelectronically stores information. The electronic storage media ofelectronic storage 132 may include one or both of system storage that isprovided integrally (i.e., substantially non-removable) with server(s)102 and/or removable storage that is removably connectable to server(s)102 via, for example, a port (e.g., a USB port, a firewire port, etc.)or a drive (e.g., a disk drive, etc.). Electronic storage 132 mayinclude one or more of optically readable storage media (e.g., opticaldisks, etc.), magnetically readable storage media (e.g., magnetic tape,magnetic hard drive, floppy drive, etc.), electrical charge-basedstorage media (e.g., EEPROM, RAM, etc.), solid-state storage media(e.g., flash drive, etc.), and/or other electronically readable storagemedia. Electronic storage 132 may include one or more virtual storageresources (e.g., cloud storage, a virtual private network, and/or othervirtual storage resources). Electronic storage 132 may store softwarealgorithms, information determined by processor(s) 134, informationreceived from server(s) 102, information received from client computingplatform(s) 104, and/or other information that enables server(s) 102 tofunction as described herein.

Processor(s) 134 may be configured to provide information processingcapabilities in server(s) 102. As such, processor(s) 134 may include oneor more of a digital processor, an analog processor, a digital circuitdesigned to process information, an analog circuit designed to processinformation, a state machine, and/or other mechanisms for electronicallyprocessing information. Although processor(s) 134 is shown in FIG. 1A asa single entity, this is for illustrative purposes only. In someimplementations, processor(s) 134 may include a plurality of processingunits. These processing units may be physically located within the samedevice, or processor(s) 134 may represent processing functionality of aplurality of devices operating in coordination. Processor(s) 134 may beconfigured to execute components 108, 110, 112, 114, and/or 116, and/orother components. Processor(s) 134 may be configured to executecomponents 108, 110, 112, 114, and/or 116, and/or other components bysoftware; hardware; firmware; some combination of software, hardware,and/or firmware; and/or other mechanisms for configuring processingcapabilities on processor(s) 134. As used herein, the term “component”may refer to any component or set of components that perform thefunctionality attributed to the component. This may include one or morephysical processors during execution of processor readable instructions,the processor readable instructions, circuitry, hardware, storage media,or any other components.

It should be appreciated that although components 108, 110, 112, 114,and/or 116 are illustrated in FIG. 1A as being implemented within asingle processing unit, in implementations in which processor(s) 134includes multiple processing units, one or more of components 108, 110,112, 114, and/or 116 may be implemented remotely from the othercomponents. The description of the functionality provided by thedifferent components 108, 110, 112, 114, and/or 116 described below isfor illustrative purposes, and is not intended to be limiting, as any ofcomponents 108, 110, 112, 114, and/or 116 may provide more or lessfunctionality than is described. For example, one or more of components108, 110, 112, 114, and/or 116 may be eliminated, and some or all of itsfunctionality may be provided by other ones of components 108, 110, 112,114, and/or 116. As an example, processor(s) 134 may be configured toexecute one or more additional components that may perform some or allof the functionality attributed below to one of components 108, 110,112, 114, and/or 116.

FIG. 1B illustrates a flowchart 150 of a method for pay characterizationof a subterranean hydrocarbon reservoir. The left column shows inputdata 152, which may include subsurface data and well data, as describedabove. Input data 152 may have corresponding production parameterscharacterizing subsurface production features, such as, for example,well attributes, as a function of position in the subsurface volume ofinterest. The subsurface data and the well data may be used to generatemultiple production parameter maps (e.g., reservoir property maps).Geological data may be gridded. Gridding methods 154, such as, forexample, cokriging may provide measurable uncertainty due tointerpolation in the form of standard error maps. The standard errormaps may be useful for considering the inclusion of a productionparameter into the parameter model (e.g., random forest algorithm) ofthe workflow. Multiple production parameters maps or reservoir propertymaps 156 may include, at a minimum, average porosity, pore saturation,mineralogy, lithofacies, geomechanical properties, organic richness,and/or any other reservoir property.

Production parameter maps 156 may be subjected to a parameter model,such as, for example, a 2D statistical analysis 158. In particular, arandom forest algorithm may be used, as described herein. Using theparameter model with the multiple production parameter maps may allowfor validation of the parameter model via blind testing. Applying theparameter model to the production parameter values may generaterepresentations 160. Individual pseudo wells, or an estimated reservoirproductivity, may be in the representation as a function of position inthe subsurface volume of interest maps and time. A given well may besubjected to a type curve generation and decline analysis 162. The typecurve generation and decline analysis 164 may identify a productivity asa function of time.

FIG. 3 illustrates example Boruta plots identifying an effect productionparameters may have on estimated reservoir productivity, in accordancewith one or more implementations. As illustrated, production parameterscloser to the right side indicate a larger effect a given productionparameter has on estimated reservoir productivity. These productionparameters may have been identified by the parameter model as beingcritical to the prediction of production, as they may tend to morefrequently lead to more robust correlations in individual regressiontrees when they are included randomly. Similar ensembles of productionparameters may be used to predict each incremental time interval ofcumulative production to build a spatial array of prediction locationsthat have all of the incremental production volumes associated. Theproduction parameter on the bottom (e.g., Res. Prop. #1) may have thegreatest effect, Res. Prop. #2 may have the second greatest effect, Res.Prop #3 may have the third greatest effect, and so on. The effect of themultiple production parameters may change as a function of time. Forexample, the Boruta plot on the left indicates that Res. Prop #4 has agreater effect at 12 months than at 6 months. The Boruta plot on theleft indicates that Comp. Prop #1 has the fifth greatest effect onestimated reservoir productivity at 12 months and has the fourthgreatest effect on estimated reservoir productivity at 6 months.

FIG. 4 illustrates example Boruta plots identifying an effect productionparameters may have on estimated reservoir productivity by month, inaccordance with one or more implementations.

FIG. 5 illustrates example production parameter graphs, in accordancewith one or more implementations. These production parameter values mayindicate the marginal effect of a given production parameter onestimated reservoir productivity. The interpretations can be used as amanual check by a subject matter expert to ensure that a givenproduction parameter has a realistic effect on well performanceprediction. Spurious or illogical production parameter graphs mayindicate under- or miss-sampling. Meaningful production parameters maybe used to make regression-based predictions of estimated reservoirproductivity at incremental time intervals as a function of position ina subsurface volume of interest. The estimated reservoir productivitymay be transformed into incremental production rates and subjected totraditional decline curve analysis. As illustrated, property #1 andproperty #3 have a threshold value above which refined productionparameter values may be limited. Property #4, Property #5, and Property#6 may indicate an increasing level of productivity as the correspondingproduction parameter values increase. Property #2 may indicate adecreasing level of productivity as the production parameter valuesincrease.

FIGS. 6A, 6B, 6C, 6D, 6E, 6F, 6G, 6H, 6I, 6J, 6K, and 6L show examplemap results of estimated reservoir productivity over a 12 monthinterval. FIGS. 7A, 7B, and 7C, 7D, and 7E illustrate example type curvegeneration and decline analyses used to estimate reservoir productivitycompared to actual productivity. Individual type curve generation anddecline analyses may correspond to differently defined well designs.

FIG. 8 is an example output of the disclosed technology, in accordancewith one or more implementations. As illustrated, the size of thespatial array may include about 217,000 positions with an estimatedreservoir productivity at each position. It should be appreciated thatthere are no inherent limitations to the spacing of the array, nor thetemporal resolution of the cumulative production predictions tied toeach array location. The output may include coordinates, productionparameter values, a defined well design, cumulative estimated reservoirproductivity in multiple time intervals, and/or other items.

FIG. 9 illustrates a method 900 for estimating reservoir productivity asa function of position in a subsurface volume of interest, in accordancewith one or more implementations. The operations of method 900 presentedbelow are intended to be illustrative. In some implementations, method900 may be accomplished with one or more additional operations notdescribed, and/or without one or more of the operations discussed.Additionally, the order in which the operations of method 900 areillustrated in FIG. 8 and described below is not intended to belimiting.

In some implementations, method 900 may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information). The one or moreprocessing devices may include one or more devices executing some or allof the operations of method 900 in response to instructions storedelectronically on an electronic storage medium. The one or moreprocessing devices may include one or more devices configured throughhardware, firmware, and/or software to be specifically designed forexecution of one or more of the operations of method 900.

An operation 902 may include obtaining, from the non-transientelectronic storage, subsurface data and well data corresponding to asubsurface volume of interest. The subsurface data and the well data mayinclude production parameter values for multiple production parametersas a function of position in the subsurface volume of interest, therebycharacterizing subsurface production features that affect the reservoirproductivity. Operation 902 may be performed by one or more hardwareprocessors configured by machine-readable instructions including acomponent that is the same as or similar to subsurface data and welldata component 108, in accordance with one or more implementations.

An operation 904 may include obtaining, from the non-transientelectronic storage, a parameter model. The parameter model may betrained using training data on an initial parameter model. The trainingdata may include well data and the production parameter values forcorresponding multiple production parameters affecting productivity ofthe one or more wells as a function of position in the subsurface volumeof interest. The parameter model may include a random forest algorithm.Operation 904 may be performed by one or more hardware processorsconfigured by machine-readable instructions including a component thatis the same as or similar to parameter model component 110, inaccordance with one or more implementations.

An operation 906 may include using, with the one or more physicalcomputer processors, the subsurface data and the well data to generatemultiple production parameter maps. A given production parameter map mayrepresent the production parameter values for a given productionparameter as a function of time and position in the subsurface volume ofinterest. Operation 906 may be performed by one or more hardwareprocessors configured by machine-readable instructions including acomponent that is the same as or similar to subsurface data and welldata component 108, in accordance with one or more implementations.

An operation 908 may include applying, with the one or more physicalcomputer processors, the parameter model to the multiple productionparameter maps to generate refined production parameter values.Operation 908 may be performed by one or more hardware processorsconfigured by machine-readable instructions including a component thatis the same as or similar to parameter model component 110, inaccordance with one or more implementations.

An operation 910 may include generating, with the one or more physicalcomputer processors, multiple refined production parameter graphs fromthe refined production parameter values wherein a given refinedproduction parameter graph specifies the refined production parametervalues for a corresponding production parameter as a function ofestimated reservoir productivity. Operation 910 may be performed by oneor more hardware processors configured by machine-readable instructionsincluding a component that is the same as or similar to productionparameter graph component 112, in accordance with one or moreimplementations.

An operation 912 may include displaying, via the graphical userinterface, the multiple refined production parameter graphs. Operation912 may be performed by one or more hardware processors configured bymachine-readable instructions including a component that is the same asor similar to production parameter graph component 112, in accordancewith one or more implementations.

An operation 914 may include generating, with the one or more physicalcomputer processors, one or more user input options to define a welldesign and limit the refined production parameter values correspondingto individual ones of the multiple refined production parameters.Operation 914 may be performed by one or more hardware processorsconfigured by machine-readable instructions including a component thatis the same as or similar to user input component 114, in accordancewith one or more implementations.

An operation 916 may include receiving, via the graphical userinterface, a defined well design the one or more user input optionsselected by a user to limit the refined production parameter valuescorresponding to the multiple refined production parameter graphs togenerate limited production parameter values. Operation 916 may beperformed by one or more hardware processors configured bymachine-readable instructions including a component that is the same asor similar to user input component 114, in accordance with one or moreimplementations.

An operation 918 may include generating, with the one or more physicalcomputer processors, a representation of estimated reservoirproductivity as a function of position in the subsurface volume ofinterest using the defined well design and visual effects to depict atleast a portion of the limited production parameter values, based on theone or more user input options selected. Operation 918 may be performedby one or more hardware processors configured by machine-readableinstructions including a component that is the same as or similar torepresentation component 116, in accordance with one or moreimplementations.

An operation 920 may include displaying, via the graphical userinterface, the representation. Operation 920 may be performed by one ormore hardware processors configured by machine-readable instructionsincluding a component that is the same as or similar to representationcomponent 116, in accordance with one or more implementations.

FIG. 10 illustrates a workflow for estimating reservoir productivity asa function of position in a subsurface volume of interest, in accordancewith one or more implementations. In part A, production parameter valuesmay be pre-filtered for statistical significance and collinearity using,for example, a Pearson correlation matrix. In part B, a Boruta plot maybe generated from the random forest model. The critical productionparameters for estimating well productivity may be identified and rankedin order of an effect on estimated reservoir productivity. At part C,there is a production parameter graph interpretation. Grids of theproduction parameters identified in the Boruta plots may be used toestimate productivity for a mapped array of reservoirs. In part D, themultiple refined production parameter graphs may be used to generatelimited production parameter values that may be combined into arepresentation. The representation may represent an estimated reservoirproductivity as a function of position in the subsurface volume ofinterest.

FIGS. 11A, 11B, 11C, and 11D illustrate example production parametergraphs, in accordance with one or more implementations. As illustrated,the effect production parameters may have over time changes. Forexample, stress changes from having the greatest effect on estimatedreservoir productivity to having the second greatest effect on estimatedreservoir productivity to having the third greatest effect on estimatedreservoir productivity. As one example, brittleness goes from having theninth greatest effect on estimated reservoir productivity to having thesecond greatest effect on estimated reservoir productivity over time.

Although the present technology has been described in detail for thepurpose of illustration based on what is currently considered to be themost practical and preferred implementations, it is to be understoodthat such detail is solely for that purpose and that the technology isnot limited to the disclosed implementations, but, on the contrary, isintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the appended Claims. For example, it isto be understood that the present technology contemplates that, to theextent possible, one or more features of any implementation can becombined with one or more features of any other implementation.

While particular implementations are described above, it will beunderstood it is not intended to limit the presently disclosedtechnology to these particular implementations. On the contrary, thepresently disclosed technology includes alternatives, modifications andequivalents that are within the spirit and scope of the appended claims.Numerous specific details are set forth in order to provide a thoroughunderstanding of the subject matter presented herein. But it will beapparent to one of ordinary skill in the art that the subject matter maybe practiced without these specific details. In other instances,well-known methods, procedures, components, and circuits have not beendescribed in detail so as not to unnecessarily obscure aspects of theimplementations.

The terminology used in the description of the presently disclosedtechnology herein is for the purpose of describing particularimplementations only and is not intended to be limiting of the presentlydisclosed technology. As used in the description of the presentlydisclosed technology and the appended claims, the singular forms “a,”“an,” and “the” are intended to include the plural forms as well, unlessthe context clearly indicates otherwise. It will be understood that theterm “and/or” as used herein refers to and encompasses any and allpossible combinations of one or more of the associated listed items. Itwill be further understood that the terms “includes,” “including,”“comprises,” and/or “comprising,” when used in this specification,specify the presence of stated features, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, operations, elements, components, and/or groups thereof.

As used herein, the term “if” may be construed to mean “when” or “upon”or “in response to determining” or “in accordance with a determination”or “in response to detecting,” that a stated condition precedent istrue, depending on the context. Similarly, the phrase “if it isdetermined [that a stated condition precedent is true]” or “if [a statedcondition precedent is true]” or “when [a stated condition precedent istrue]” may be construed to mean “upon determining” or “in response todetermining” or “in accordance with a determination” or “upon detecting”or “in response to detecting” that the stated condition precedent istrue, depending on the context.

Although some of the various drawings illustrate a number of logicalstages in a particular order, stages that are not order dependent may bereordered and other stages may be combined or broken out. While somereordering or other groupings are specifically mentioned, others will beobvious to those of ordinary skill in the art and so do not present anexhaustive list of alternatives. Moreover, it should be recognized thatthe stages could be implemented in hardware, firmware, software or anycombination thereof.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific implementations. However, theillustrative discussions above are not intended to be exhaustive or tolimit the presently disclosed technology to the precise forms disclosed.Many modifications and variations are possible in view of the aboveteachings. The implementations were chosen and described in order tobest explain the principles of the presently disclosed technology andits practical applications, to thereby enable others skilled in the artto best utilize the presently disclosed technology and variousimplementations with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A computer-implemented method for estimatingreservoir productivity as a function of position in a subsurface volumeof interest, the method being implemented in a computer system thatincludes one or more physical computer processors, non-transientelectronic storage, and a graphical user interface, comprising:obtaining, from the non-transient electronic storage, subsurface dataand well data corresponding to a subsurface volume of interest, whereinthe subsurface data and the well data include production parametervalues for multiple production parameters as a function of position inthe subsurface volume of interest, thereby characterizing subsurfaceproduction features that affect the reservoir productivity; obtaining,from the non-transient electronic storage, a parameter model, theparameter model having been conditioned by training an initial parametermodel using training data, wherein the training data includes (i) thewell data of one or more wells in the subsurface volume of interest, and(ii) the production parameter values for corresponding multipleproduction parameters affecting productivity of the one or more wells asa function of position in the subsurface volume of interest; using, withthe one or more physical computer processors, the subsurface data andthe well data to generate multiple production parameter maps, wherein agiven production parameter map represents the production parametervalues for a given production parameter as a function of time andposition in the subsurface volume of interest; applying, with the one ormore physical computer processors, the parameter model to the multipleproduction parameter maps to generate refined production parametervalues; generating, with the one or more physical computer processors,multiple refined production parameter graphs from the refined productionparameter values, wherein a given refined production parameter graphspecifies the refined production parameter values for a correspondingproduction parameter as a function of estimated reservoir productivity;displaying, via the graphical user interface, the multiple refinedproduction parameter graphs; generating, with the one or more physicalcomputer processors, one or more user input options to define a welldesign and to limit the refined production parameter valuescorresponding to individual ones of the multiple refined productionparameters; receiving, via the graphical user interface, a defined welldesign and the one or more user input options selected by a user tolimit the refined production parameter values corresponding to themultiple refined production parameter graphs to generate limitedproduction parameter values; based on the one or more user input optionsselected, generating, with the one or more physical computer processors,a representation of an estimated reservoir productivity as a function ofposition in the subsurface volume of interest using the defined welldesign and visual effects to depict at least a portion of the limitedproduction parameter values as a function of position in the subsurfacevolume of interest; and displaying, via the graphical user interface,the representation.
 2. The computer-implemented method of claim 1,further comprising presenting, via the graphical user interface, the oneor more user input options corresponding to the multiple refinedproduction parameters.
 3. The computer-implemented method of claim 1,wherein the user input options comprise a threshold value to limit therefined production parameter values.
 4. The computer-implemented methodof claim 1, wherein the user input options comprise a positive ornegative rate of change for the multiple refined production parametergraphs to limit the refined production parameter values.
 5. Thecomputer-implemented method of claim 1, wherein the parameter modelcomprises random forest machine learning.
 6. The computer-implementedmethod of claim 1, wherein the production parameter values are filteredusing a Pearson correlation matrix.
 7. The computer-implemented methodof claim 1, wherein the representation changes as a function of time. 8.A computer-implemented method for estimating production as a function ofposition in a subsurface volume of interest, the method beingimplemented in a computer system that includes one or more physicalcomputer processors, non-transient electronic storage, and a graphicaluser interface, comprising: obtaining, from the non-transient electronicstorage, subsurface data and well data corresponding to a subsurfacevolume of interest, wherein the subsurface data and the well datainclude production parameter values for multiple production parametersas a function of position in the subsurface volume of interest, therebycharacterizing subsurface production features that affect the reservoirproductivity; obtaining, from the non-transient electronic storage, aparameter model, the parameter model having been conditioned by trainingan initial parameter model using training data, wherein the trainingdata includes (i) the well data of one or more wells in the subsurfacevolume of interest, and (ii) the production parameter values forcorresponding multiple production parameters affecting productivity ofthe one or more wells as a function of position in the subsurface volumeof interest; using, with the one or more physical computer processors,the subsurface data and the well data to generate multiple productionparameter maps, wherein a given production parameter map represents theproduction parameter values for a given production parameter as afunction of time and position in the subsurface volume of interest;applying, with the one or more physical computer processors, theparameter model to the multiple production parameter maps to generaterefined production parameter values; generating, with the one or morephysical computer processors, multiple refined production parametergraphs from the refined production parameter values, wherein a givenrefined production parameter graph specifies the refined productionparameter values for a corresponding production parameter as a functionof estimated reservoir productivity; and displaying, via the graphicaluser interface, the multiple refined production parameter graphs.
 9. Thecomputer-implemented method of claim 8, further comprising: generating,with the one or more physical computer processors, one or more userinput options to define a well design and to limit the refinedproduction parameter values corresponding to individual ones of themultiple refined production parameters; receiving, via the graphicaluser interface, a defined well design and the one or more user inputoptions selected by a user to limit the refined production parametervalues corresponding to the multiple refined production parameter graphsto generate limited production parameter values; based on the one ormore user input options selected, generating, with the one or morephysical computer processors, a representation of an estimated reservoirproductivity as a function of position in the subsurface volume ofinterest using the defined well design and visual effects to depict atleast a portion of the limited production parameter values as a functionof position in the subsurface volume of interest; and displaying, viathe graphical user interface, the representation.
 10. Thecomputer-implemented method of claim 10, further comprising presenting,via the graphical user interface, the one or more user input optionscorresponding to the multiple refined production parameters.
 11. Thecomputer-implemented method of claim 10, wherein the user input optionscomprise a threshold value to limit the refined production parametervalues.
 12. The computer-implemented method of claim 10, wherein theuser input options comprise a positive or negative rate of change forthe multiple refined production parameter graphs to limit the refinedproduction parameter values.
 13. The computer-implemented method ofclaim 9, further comprising: limiting, with the one or more physicalcomputer processors, the refined production parameter values usingthreshold values and rates of changes to generate limited productionparameter values; generating, with the one or more physical computerprocessors, a representation of an estimated production of a well in thesubsurface volume of interest using a defined well design and visualeffects to depict at least a portion of the limited production parametervalues as a function of position in the subsurface volume of interest;and displaying, via the graphical user interface, the representation.14. The computer-implemented method of claim 13, wherein limiting therefined production parameter values comprises applying linear analysis,non-linear analysis, machine learning, and/or rate of change analysis tothe refined production parameter values.
 15. The computer-implementedmethod of claim 9, wherein the parameter model comprises random forestmachine learning.
 16. The computer-implemented method of claim 9,wherein individual ones of the multiple production parameter mapsillustrate a corresponding parameter of the subsurface data and welldata as a function of position in the subsurface volume of interest. 17.The computer-implemented method of claim 10 or 13, wherein therepresentation changes as a function of time.
 18. A system configuredfor estimating reservoir productivity as a function of position in asubsurface volume of interest, the system comprising: non-transientelectronic storage; a graphical user interface; and one or more physicalcomputer processors configured by machine-readable instructions to:obtain, from the non-transient electronic storage, subsurface data andwell data corresponding to a subsurface volume of interest, wherein thesubsurface data and the well data include production parameter valuesfor multiple production parameters as a function of position in thesubsurface volume of interest, thereby characterizing subsurfaceproduction features that affect the reservoir productivity; obtain, fromthe non-transient electronic storage, a parameter model, the parametermodel having been conditioned by training an initial parameter modelusing training data, wherein the training data includes (i) the welldata of one or more wells in the subsurface volume of interest, and (ii)the production parameter values for corresponding multiple productionparameters affecting productivity of the one or more wells as a functionof position in the subsurface volume of interest; use, with the one ormore physical computer processors, the subsurface data and the well datato generate multiple production parameter maps, wherein a givenproduction parameter map represents the production parameter values fora given production parameter as a function of time and position in thesubsurface volume of interest; apply, with the one or more physicalcomputer processors, the parameter model to the multiple productionparameter maps to generate refined production parameter values;generate, with the one or more physical computer processors, multiplerefined production parameter graphs from the refined productionparameter values wherein a given refined production parameter graphspecifies the refined production parameter values for a correspondingproduction parameter as a function of estimated reservoir productivity;display, via the graphical user interface, the multiple refinedproduction parameter graphs; generate, with the one or more physicalcomputer processors, one or more user input options to define a welldesign and to limit the refined production parameter valuescorresponding to individual ones of the multiple refined productionparameters; receive, via the graphical user interface, a defined welldesign and the one or more user input options selected by a user tolimit the refined production parameter values corresponding to themultiple refined production parameter graphs to generate limitedproduction parameter values; based on the one or more user input optionsselected, generate, with the one or more physical computer processors, arepresentation of an estimated reservoir productivity as a function ofposition in the subsurface volume of interest using the defined welldesign and visual effects to depict at least a portion of the limitedproduction parameter values as a function of position in the subsurfacevolume of interest; and display, via the graphical user interface, therepresentation.
 19. The system of claim 18, further comprisingpresenting, via the graphical user interface, the one or more user inputoptions corresponding to the multiple refined production parameters. 20.The system of claim 18, wherein the representation changes as a functionof time.